---
title: Model Context Protocol (MCP)
sidebarTitle: Overview
description: Learn how to use MCP with Agno to enable your agents to interact with external systems through a standardized interface.
---

The [Model Context Protocol (MCP)](https://modelcontextprotocol.io) enables Agents to interact with external systems through a standardized interface.
You can connect your Agents to any MCP server, using Agno's MCP integration.

Below is a simple example shows how to connect an Agent to the Agno MCP server:

```python
from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.mcp import MCPTools

# Create the Agent
agno_agent = Agent(
    name="Agno Agent",
    model=Claude(id="claude-sonnet-4-0"),
    # Add the Agno MCP server to the Agent
    tools=[MCPTools(transport="streamable-http", url="https://docs.agno.com/mcp")],
)
```

## The Basic Flow

<Steps>
    <Step title="Find the MCP server you want to use">
    You can use any working MCP server. To see some examples, you can check [this GitHub repository](https://github.com/modelcontextprotocol/servers), by the maintainers of the MCP themselves.
    </Step>

    <Step title="Initialize the MCP integration">
    Initialize the `MCPTools` class and connect to the MCP server. 
    The recommended way to define the MCP server is to use the `command` or `url` parameters. 
    With `command`, you can pass the command used to run the MCP server you want. With `url`, you can pass the URL of the running MCP server you want to use.

    For example, to connect to the Agno documentation MCP server, you can do the following:

    ```python
    from agno.tools.mcp import MCPTools

    # Initialize and connect to the MCP server
    mcp_tools = MCPTools(transport="streamable-http", url="https://docs.agno.com/mcp"))
    await mcp_tools.connect()
    ```

    </Step>

    <Step title="Provide the MCPTools to the Agent">
    When initializing the Agent, pass the `MCPTools` instance in the `tools` parameter. Remember to close the connection when you're done.

    The agent will now be ready to use the MCP server:

    ```python
    from agno.agent import Agent
    from agno.models.openai import OpenAIChat
    from agno.tools.mcp import MCPTools

    # Initialize and connect to the MCP server
    mcp_tools = MCPTools(url="https://docs.agno.com/mcp")
    await mcp_tools.connect()

    try:
        # Setup and run the agent
        agent = Agent(model=OpenAIChat(id="gpt-5-mini"), tools=[mcp_tools])
        await agent.aprint_response("Tell me more about MCP support in Agno", stream=True)
    finally:
        # Always close the connection when done
        await mcp_tools.close()
    ```
    </Step>

</Steps>


### Example: Filesystem Agent

Here's a filesystem agent that uses the [Filesystem MCP server](https://github.com/modelcontextprotocol/servers/tree/main/src/filesystem) to explore and analyze files:

```python filesystem_agent.py
import asyncio
from pathlib import Path
from textwrap import dedent

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.mcp import MCPTools


async def run_agent(message: str) -> None:
    """Run the filesystem agent with the given message."""

    file_path = "<path to the directory you want to explore>"

    # Initialize and connect to the MCP server to access the filesystem
    mcp_tools = MCPTools(command=f"npx -y @modelcontextprotocol/server-filesystem {file_path}")
    await mcp_tools.connect()

    try:
        agent = Agent(
            model=OpenAIChat(id="gpt-5-mini"),
            tools=[mcp_tools],
            instructions=dedent("""\
                You are a filesystem assistant. Help users explore files and directories.

                - Navigate the filesystem to answer questions
                - Use the list_allowed_directories tool to find directories that you can access
                - Provide clear context about files you examine
                - Use headings to organize your responses
                - Be concise and focus on relevant information\
            """),
            markdown=True,
        )

        # Run the agent
        await agent.aprint_response(message, stream=True)
    finally:
        # Always close the connection when done
        await mcp_tools.close()


# Example usage
if __name__ == "__main__":
    # Basic example - exploring project license
    asyncio.run(run_agent("What is the license for this project?"))
```

## Connecting your MCP server

### Using `connect()` and `close()`

It is recommended to use the `connect()` and `close()` methods to manage the connection lifecycle of the MCP server.

```python
mcp_tools = MCPTools(command="uvx mcp-server-git")
await mcp_tools.connect()
```

After you're done, you should close the connection to the MCP server.

```python
await mcp_tools.close()
```

<Check>
This is the recommended way to manage the connection lifecycle of the MCP server when using `Agent` or `Team` instances.
</Check>

### Automatic Connection Management

If you pass the `MCPTools` instance to the `Agent` or `Team` instances without first calling `connect()`, the connection will be managed automatically.

For example:

```python
mcp_tools = MCPTools(command="uvx mcp-server-git")
agent = Agent(model=OpenAIChat(id="gpt-5-mini"), tools=[mcp_tools])
await agent.aprint_response("What is the license for this project?", stream=True)  # The connection is established and closed on each run.
```

<Note>
Here the connection to the MCP server (in the case of hosted MCP servers) is established and closed on each run.
Additionally the list of available tools is refreshed on each run.

This has an impact on performance and is not recommended for production use.
</Note>

### Using Async Context Manager

If you prefer, you can also use `MCPTools` or `MultiMCPTools` as async context managers for automatic resource cleanup:

```python
async with MCPTools(command="uvx mcp-server-git") as mcp_tools:
    agent = Agent(model=OpenAIChat(id="gpt-5-mini"), tools=[mcp_tools])
    await agent.aprint_response("What is the license for this project?", stream=True)
```

This pattern automatically handles connection and cleanup, but the explicit `.connect()` and `.close()` methods provide more control over connection lifecycle.

### Automatic Connection Management in AgentOS

When using MCPTools within AgentOS, the lifecycle is automatically managed. No need to manually connect or disconnect the `MCPTools` instance. This does not automatically refresh connections, you can use [`refresh_connection`](#connection-refresh) to do so.

See the [AgentOS + MCPTools](/agent-os/mcp/tools) page for more details.

<Check>
This is the recommended way to manage the connection lifecycle of the MCP server when using `AgentOS`.
</Check>

## Connection Refresh

You can set `refresh_connection` on the `MCPTools` and `MultiMCPTools` instances to refresh the connection to the MCP server on each run.

```python
mcp_tools = MCPTools(command="uvx mcp-server-git", refresh_connection=True)
await mcp_tools.connect()

agent = Agent(model=OpenAIChat(id="gpt-5-mini"), tools=[mcp_tools])
await agent.aprint_response("What is the license for this project?", stream=True)  # The connection will be refreshed on each run.

await mcp_tools.close()
```

### How it works

- When you call the `connect()` method, a new session is established with the MCP server. If that server becomes unavailable, that connection is closed and a new one has to be established.
- If you set `refresh_connection` to `True`, each time the agent is run the connection to the MCP server is checked and re-established if needed, and the list of available tools is then refreshed.
- This is particularly useful for hosted MCP servers that are prone to restarts or that often change their schema or list of tools.
- It is recommended to only use this when you manually manage the connection lifecycle of the MCP server, or when using agents/teams with [`MCPTools` in `AgentOS`](/agent-os/mcp/tools).

## Transports

Transports in the Model Context Protocol (MCP) define how messages are sent and received. The Agno integration supports the three existing types:
- [stdio](https://modelcontextprotocol.io/docs/concepts/transports#standard-input%2Foutput-stdio) -> See the [stdio transport documentation](/concepts/tools/mcp/transports/stdio)
- [Streamable HTTP](https://modelcontextprotocol.io/specification/draft/basic/transports#streamable-http) -> See the [streamable HTTP transport documentation](/concepts/tools/mcp/transports/streamable_http)
- [SSE](https://modelcontextprotocol.io/docs/concepts/transports#server-sent-events-sse) -> See the [SSE transport documentation](/concepts/tools/mcp/transports/sse)

<Note>
The stdio (standard input/output) transport is the default one in Agno's `MCPTools` and `MultiMCPTools`. 
</Note>


## Best Practices

1. **Resource Cleanup**: Always close MCP connections when done to prevent resource leaks:
```python
mcp_tools = MCPTools(command="uvx mcp-server-git")
await mcp_tools.connect()

try:
    # Your agent code here
    pass
finally:
    await mcp_tools.close()
```

2. **Error Handling**: Always include proper error handling for MCP server connections and operations.

3. **Clear Instructions**: Provide clear and specific instructions to your agent:

```python
instructions = """
You are a filesystem assistant. Help users explore files and directories.
- Navigate the filesystem to answer questions
- Use the list_allowed_directories tool to find accessible directories
- Provide clear context about files you examine
- Be concise and focus on relevant information
"""
```

## Developer Resources

- See how to use MCP with AgentOS [here](/agent-os/mcp/tools).
- Find examples of Agents that use MCP [here](/examples/concepts/tools/mcp/airbnb).
- Find a collection of MCP servers [here](https://github.com/modelcontextprotocol/servers).
